Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach

Article


Thiruchittampalam, Sureka, Banerjee, Bikram Pratap, Glenn, Nancy F. and Raval, Simit. 2024. "Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach." Engineering Geology. 329. https://doi.org/10.1016/j.enggeo.2024.107406
Article Title

Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach

ERA Journal ID1722
Article CategoryArticle
AuthorsThiruchittampalam, Sureka, Banerjee, Bikram Pratap, Glenn, Nancy F. and Raval, Simit
Journal TitleEngineering Geology
Journal Citation329
Article Number107406
Year2024
PublisherElsevier
Place of PublicationNetherlands
ISSN0013-7952
1872-6917
Digital Object Identifier (DOI)https://doi.org/10.1016/j.enggeo.2024.107406
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0013795224000048
Abstract

Geotechnical characterisation of spoil piles has traditionally relied on the expertise of field specialists, which can be both hazardous and time-consuming. Although unmanned aerial vehicles (UAV) show promise as a remote sensing tool in various applications; accurately segmenting and classifying very high-resolution remote sensing images of heterogeneous terrains, such as mining spoil piles with irregular morphologies, presents significant challenges. The proposed method adopts a robust approach that combines morphology-based segmentation, as well as spectral, textural, structural, and statistical feature extraction techniques to overcome the difficulties associated with spoil pile characterisation. Additionally, it incorporates minimum redundancy maximum relevance (mRMR) based feature selection and machine learning-based classification. This automated characterisation will serve as a proactive tool for dump stability assessment, providing crucial data for improved stability models and contributing to a greener and more responsible mining industry.

KeywordsObject-based image analysis; Morphology-based segmentation; Waste materials; Mine dump; High-resolution UAV images; Shear strength parameters
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
401905. Mining engineering
Byline AffiliationsUniversity of New South Wales
University of Moratuwa, Sri Lanka
School of Surveying and Built Environment
Boise State University, United States
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